1. Exploring pre-trained LLMs
Welcome to this video, where we will explore how to use Large Language Models, or LLMs, as the starting point in the RLHF process.
2. The importance of fine-tuning
We've seen how, in the process of RLHF,
3. The importance of fine-tuning
the initial LLM is a central component, and it is important that, by the time the human evaluator comes in, the outputs are already relevant to the problem that the LLM is being trained to solve. This is achieved through fine-tuning, which is our focus in this video.
4. A step-by-step guide to fine-tuning an LLM
Imagine working with a model to infer the sentiments of tweets
5. A step-by-step guide to fine-tuning an LLM
and noticing it's not very effective. Before moving to the next steps in the RLHF process, we need to improve its performance.
6. A step-by-step guide to fine-tuning an LLM
The first step in fine-tuning is to select a pre-trained model and a relevant dataset. By using the pre-trained model and training it on a dataset of tweets labeled with their sentiments, we can significantly enhance its accuracy in sentiment analysis.
7. Step 1: load the data to use
First, we need high-quality data, which is where the datasets library comes in.
We use the Hugging Face datasets library to import a dataset containing tweets categorized by sentiment: positive, neutral, or negative.
8. Step 2: choose a pre-trained model
To fine-tune a model, it's essential to start with a pre-trained model.
Here, we use AutoModelForCausalLM. Causal language models are generally decoder-only models. These models look at past tokens to predict the next token. With decoder-only language models, we can think of the next token prediction process as "causal language modeling" because the previous tokens "cause" each subsequent token.
9. Step 3: tokenizer
Now that we have our dataset, we need to preprocess it with a tokenizer to convert it into numerical tokens that our model can understand. We use AutoTokenizer to load a pre-trained tokenizer for our model.
First, we add a special padding token with 'add_special_tokens'. This is important for handling inputs of different lengths, so the model can process them as equal-sized batches. After adding the token, we resize the model's embeddings with 'resize_token_embeddings' to match the updated tokenizer, allowing the model to recognize the new token.
10. Step 3: tokenizer
Next, we define a function to convert the text field into token IDs, that we apply to the entire dataset using map. Setting batched equal to 'True' makes processing faster by working on multiple examples at once.
11. Step 4: fine-tune using the Trainer method
Our final step is to set up the training arguments and start the training process. First, we create TrainingArguments, where we specify where to save the model, set the batch size for both training and evaluation, and use 'gradient_accumulation_steps' equal to 4 to accumulate gradients over four steps before updating the model. This helps simulate a larger batch size without using too much memory.
Next, we create a Trainer object, which handles the training loop. We pass in the model, the training arguments, and both the tokenized training and evaluation datasets. Once everything is set, we can train the model using the dot train method.
12. Let's practice!
Now that we've seen the step-by-step process of fine-tuning a model, it's your turn to dive in. Go ahead and start experimenting: practice makes perfect!